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Salary: USD 244,000 - 366,000 / annual
Klaviyo is seeking a Principal Software Engineer to own the technical direction and delivery of its data platform—streaming, batch compute, storage/lakehouse, and governance. This is a high-impact individual-contributor role (no direct reports) where you will lead through architecture, code, and influence.
Klaviyo's data platform processes billions of events daily across billions of consumer profiles for hundreds of thousands of brands. The platform is increasingly consumed not just by people but by AI agents acting on customers' behalf, requiring systems that scale on two axes: ever-larger data volumes and a steady stream of new data scenarios (new sources, domains, and consumption patterns).
You will design and ship systems that are fast, reliable, cost-efficient, and safe for autonomous access, creating paved roads for data producers and consumers, human and agent alike.
**Key Responsibilities:**
- Design and implement core data platform capabilities including event ingestion/CDC, stream processing, batch orchestration, data lake/warehouse patterns, catalog/lineage, governance, access, and compliance.
- Define and uphold SLOs for data freshness, availability, and correctness; author and run readiness reviews, incident response, and post-incident learning for your domain.
- Author ADRs/RFCs, land data contracts and schema governance, and standardize connectors and templates that accelerate developer velocity.
- Profile, tune, and right-size systems for performance and cost; partner with FinOps on unit-economics guardrails.
- Pair with product teams and analytics/ML to expose the right abstractions and unblock customer value quickly.
- Contribute high-quality code and reviews; mentor Staff/Senior engineers across pillars through example and enablement (not line management).
- Use AI to streamline data workflows, from authoring and testing pipelines to catalog/search and data quality, enabling analysts, ML, and product teams to move faster with confidence.
**Success Metrics (6–12 months):**
- Adopted "paved roads" for producers/consumers with ≥99.9% freshness SLO for key domains and measurable cost/TB reductions.
- One or more step-change improvements (e.g., 2× faster ingest-to-analytics latency) demonstrated with metrics and post-incident trend-down.
**Requirements:**
- 10+ years building and operating distributed data systems (e.g., Kafka/PubSub, Flink/Spark/Beam, Airflow/Dagster, Iceberg/Delta/Hudi, Snowflake/BigQuery, object storage) with multi-tenant reliability.
- Technical expertise in data ingestion/CDC, stream processing, batch orchestration, lakehouse patterns, catalog/lineage, governance, and access controls, measured by freshness, availability, and correctness SLOs.
- Experience applying ML/GenAI to data platforms—semantic catalog search, auto-docs, data-quality anomaly detection, SQL/pipeline generation—with human-in-the-loop review and privacy controls.
- Demonstrated ability to land data contracts, connectors, and templates that speed delivery for producers and consumers; mentor via design docs and pairing.
- AI fluency: experiment, learn fast, and share AI wins responsibly.
**Nice to Haves:**
- Regional isolation/replication strategies, privacy-by-design, and data governance in regulated contexts.
- Experience with SLOs ≥99.9% freshness for key domains, measurable cost/TB reductions, and faster production debugging with defined readiness reviews and incident learning.
- AI-augmented data operations: semantic discovery and auto-documentation covering high-value datasets; AI-assisted data-quality monitors reducing data incidents by 25–40%; pipeline authoring and review times dropping 15–25% with AI in the loop.
